On the Semantics of Distributed Reactive Programming: The Cost of Consistency

The reactive programming paradigm aims to simplify the development of reactive systems. It provides abstractions to define time-changing values that are automatically updated by the runtime according to their dependencies. The benefits of reactive programming in distributed settings have been recognized for long. Yet, existing solutions for distributed reactive programming enforce the same semantics as in single processes, introducing communication and synchronization costs that hamper scalability. Establishing suitable abstractions for distributed reactive programming demands for a deeper investigation of the semantics of change propagation. This paper takes a foundational approach and defines precise propagation semantics in terms of consistency guarantees that constrain the order and isolation of value updates. We study the benefits and costs of these consistency guarantees both theoretically and empirically, using case studies and synthetic benchmarks. We show that different applications require different levels of consistency and that manually implementing the required level on a middleware that provides a lower one annuls the abstraction improvements of reactive programming. This motivates a framework that enables the developers to select the best trade-off between consistency and overhead for the problem at hand. To this end, we present DREAM, a distributed reactive programming middleware with flexible consistency guarantees